Publications

2024

1. Identifying Relevant Data in RDF Sources

   Zoé Chevallier, Zoubida Kedad, Béatrice Finance, and Frédéric Chaillan

   In Research Challenges in Information Science - 18th International Conference, RCIS 2024, Guimarães, Portugal, May 14-17, 2024, Proceedings, Part II, 2024

   Abs DOI

   The increasing number of RDF data sources published on the web represents an unprecedented amount of information. However, querying these sources to extract the relevant information for a specific need represented by a target schema is a complex task as the alignment between the target and the source schemas might not be provided or incomplete. This paper presents an approach which aims at automatically populating the classes of a target schema. Our approach relies on a semi-supervised learning algorithm that iteratively identifies instance patterns in the data source that represent candidate instances for the target schema. We present some preliminary experiments showing the effectiveness of our approach.

2. Generating SPARQL Queries for Data Discovery

   Zoé Chevallier, Zoubida Kedad, Béatrice Finance, and Frédéric Chaillan

   In Advances in Databases and Information Systems - 28th European Conference, ADBIS 2024, Bayonne, France, August 28-31, 2024, Proceedings, 2024

   Abs DOI

   The number of datasets available on the Web represents a huge space of meaningful information. Then done manually, identifying the relevant portion of the data for a specific need is time-consuming. This paper presents an approach that identifies and extracts the relevant portion of RDF data sources for some application requirements described by a target schema. The identification process is based on a semi-supervised learning algorithm that iteratively computes for each class of the target schema the similarity between source entities and both the class description and the candidate instance patterns already identified for this class. The extraction process generates SPARQL queries for the identified candidate instance patterns. We present some evaluations showing the effectiveness of our approach.

3. Data Search and Discovery in RDF Sources

   Zoé Chevallier, Zoubida Kedad, Béatrice Finance, and Frédéric Chaillan

   In The Semantic Web: ESWC 2024 Satellite Events - Hersonissos, Crete, Greece, May 26-30, 2024, Proceedings, Part I, 2024

   Abs DOI

   The RDF data sources published on the Web represent an unprecedented amount of knowledge. However, querying these sources to extract the relevant information for some specific needs represented by a target schema is a complex task, as the alignment between the target and the source schemas might not be provided or may be incomplete. This paper presents a system that aims to automatically populate the classes of a target schema from RDF data sources by identifying candidate instance patterns. This identification process relies on a semi-supervised learning algorithm and the system automatically generates the SPARQL queries that populate the target schema.

2020

1. A Reference Architecture for Smart Building Digital Twin

   Zoé Chevallier, Béatrice Finance, and Benjamin Cohen Boulakia

   In Proceedings of the International Workshop on Semantic Digital Twins co-located with the 17th Extended Semantic Web Conference, SeDiT@ESWC 2020, Heraklion, Greece, June 3, 2020, 2020

   Abs

   This paper proposes a software architecture for creating and managing unambiguous descriptions of a Smart Building, allowing its Digital Twin to be deployed. These descriptions include static semantic structural description and dynamic time-series sensors data. Moreover, it is designed to be able to include data from various simulation tools, represented in the same way as sensors data. Thus, relying on these descriptions allow easy implementations of both monitoring and interaction tools that can the be interfaced with the Smart Building. Before the recent emergence of Smart Buildings, there was very few advantages in applying this concept to the field of construction and building management. Non-connected buildings don’t produce real-time data, and can’t be monitored nor operated from computer systems. But now that more and more buildings include IoT devices that allow them to expose data and services, the benefits of creating a Digital Twin to interact with the building, without interrogating the object itself, only by querying its Digital Twin, appears obvious. It then becomes crucial to be able to expose both a static description of the building’s components (including IoT devices structural and functional description) and dynamic datathat represent the evolution of the building’s states over time.